Predicting Mass Casualty Events from 911 Data Streams
Final report
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Scripts
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| Scope And Content | The scripts folder contains both a frontend directory, backend directory, and a start shell script. The frontend contains the full react framework for the application. The backend contains the processing for the data (which is not provided), and sets up the flask server to serve the data to the frontend. There's a start.sh script that will run the frontend and backend portions together. To run this script, you will need to acquire the permission for data usage, and adjust the pointed data in main.py respectively. |
| Technical Details | You will need Python, Flask, and React to make this work. Start the shell script within the folder and that will download your requirements. |
- Collection
- Cite This Work
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Vanhook, Christopher J.; Kim, Juhwan; Damte, Selamawit; Zaslavsky, Ilya (2024). Predicting Mass Casualty Events from 911 Data Streams. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0MC907C
- Description
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Predicting Mass Casualty Events From 911 Data Streams aims to provide dispatchers with a usable tool that alerts of any potentially occurring events while allowing them to monitor call conditions and real-time traffic alerts. The data involved was call data provided by the Communications Venture Corporation. This project was accomplished as part of the DSE MAS 24 program, for the class of DSE260.
- Date Collected
- 2024-01-08 to 2024-06-08
- Date Issued
- 2024
- Advisor
- Contributors
- Note
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This project relies on external software packages, modules/libraries, or programs, use of which may carry specific license requirements. Users should comply with any licenses specified within the contents of this project.
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- Language
- English
- License
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Creative Commons Attribution 4.0 International Public License
- Rights Holder
- Vanhook, Christopher J.; Kim, Juhwan; Damte, Selamawit
- Copyright
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Under copyright (US)
Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.
Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work.
- Digital Object Made Available By
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Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp)
- Last Modified
2024-07-10